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Record W1485883549 · doi:10.1111/ajt.12133

Donor-Derived Bacteremia in Liver Transplant Recipients Despite Antibiotic Prophylaxis

2013· article· en· W1485883549 on OpenAlexaffabout
Karen Doucette, Manal Alsaif, Norman Kneteman, Linda Chui, Gregory J. Tyrrell, Deepali Kumar, Atul Humar

Bibliographic record

VenueAmerican Journal of Transplantation · 2013
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsProvincial Laboratory of Public HealthAlberta Health ServicesUniversity of Alberta
Fundersnot available
KeywordsBacteremiaMedicineAntimicrobialAntibiotic prophylaxisTransmission (telecommunications)AntibioticsStaphylococcus aureusImmunologyInternal medicineMicrobiologyBacteriaBiology

Abstract

fetched live from OpenAlex

As the disparity between the number of candidates listed for transplant and the number of donors continues to grow, marginal organ donors are increasingly utilized. This includes bacteremic donors which may carry an increased risk of transmission of infection. It is recommended that recipients of organs from bacteremic donors receive antibiotic prophylaxis based on the susceptibilities of the donor isolate to prevent transmission. Here, we present four cases of donor-derived bacteremia, despite appropriate antimicrobial prophylaxis, in four liver transplant recipients. Transmitted pathogens included Staphylococcus aureus in two cases, and Escherichia coli and Group B Streptococcus each in one case. Interestingly, none of the nonhepatic organs (n=10) utilized from these bacteremic donors resulted in transmissions. These cases highlight the fact that risk of transmission from bacteremic donors is not eliminated with antimicrobial therapy in the donor and recipient. As no transmissions occurred in recipients of nonhepatic organs from these donors, these cases also suggest that liver recipients may be at higher risk of donor transmitted bacteremia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.254
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations40
Published2013
Admission routes2
Has abstractyes

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